


import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format='%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S',
    level=os.environ.get('LOGLEVEL', 'INFO').upper(),
    stream=sys.stdout,
)

a       	=  logging.getLogger(__name__)

a       	=  {'facebook/bart-base': BartForConditionalGeneration}
a       	=  {'facebook/bart-base': BartTokenizer}


def       UpperCAmelCase_					(   ):
					lowercase_					=      argparse.ArgumentParser(description="""Export Bart model + Beam Search to ONNX graph."""	)
					parser.add_argument(
					    """--validation_file"""    ,				type=lowercase_    ,				default=lowercase_    ,				help="""A csv or a json file containing the validation data."""	)
					parser.add_argument(
					    """--max_length"""    ,				type=lowercase_    ,				default=5    ,				help="""The maximum total input sequence length after tokenization."""    ,				)
					parser.add_argument(
					    """--num_beams"""    ,				type=lowercase_    ,				default=lowercase_    ,				help=(
					        """Number of beams to use for evaluation. This argument will be """
					        """passed to ``model.generate``, which is used during ``evaluate`` and ``predict``."""
					    )    ,				)
					parser.add_argument(
					    """--model_name_or_path"""    ,				type=lowercase_    ,				help="""Path to pretrained model or model identifier from huggingface.co/models."""    ,				required=lowercase_    ,				)
					parser.add_argument(
					    """--config_name"""    ,				type=lowercase_    ,				default=lowercase_    ,				help="""Pretrained config name or path if not the same as model_name"""    ,				)
					parser.add_argument(
					    """--device"""    ,				type=lowercase_    ,				default="""cpu"""    ,				help="""Device where the model will be run"""    ,				)
					parser.add_argument("""--output_file_path"""    ,				type=lowercase_    ,				default=lowercase_    ,				help="""Where to store the final ONNX file."""	)

					lowercase_					=      parser.parse_args()

					return args


def       UpperCAmelCase_					(   UpperCAmelCase__    ,				UpperCAmelCase__="cpu"	):
					lowercase_					=      model_dict[model_name].from_pretrained(lowercase_	).to(lowercase_	)
					lowercase_					=      tokenizer_dict[model_name].from_pretrained(lowercase_	)

					if model_name in ["facebook/bart-base"]:
										lowercase_					=      0
										lowercase_					=      None
										lowercase_					=      0

					return huggingface_model, tokenizer


def       UpperCAmelCase_					(   UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__	):
					model.eval()

					lowercase_					=      None
					lowercase_					=      torch.jit.script(BARTBeamSearchGenerator(lowercase_	)	)

					with torch.no_grad():
										lowercase_					=      """My friends are cool but they eat too many carbs."""
										lowercase_					=      tokenizer([ARTICLE_TO_SUMMARIZE]    ,				max_length=1_0_2_4    ,				return_tensors="""pt"""	).to(model.device	)

										lowercase_					=      model.generate(
										    inputs["""input_ids"""]    ,				attention_mask=inputs["""attention_mask"""]    ,				num_beams=lowercase_    ,				max_length=lowercase_    ,				early_stopping=lowercase_    ,				decoder_start_token_id=model.config.decoder_start_token_id    ,				)

										torch.onnx.export(
										    lowercase_    ,				(
										        inputs["""input_ids"""],
										        inputs["""attention_mask"""],
										        num_beams,
										        max_length,
										        model.config.decoder_start_token_id,
										    )    ,				lowercase_    ,				opset_version=1_4    ,				input_names=["""input_ids""", """attention_mask""", """num_beams""", """max_length""", """decoder_start_token_id"""]    ,				output_names=["""output_ids"""]    ,				dynamic_axes={
										        """input_ids""": {0: """batch""", 1: """seq"""},
										        """output_ids""": {0: """batch""", 1: """seq_out"""},
										    }    ,				example_outputs=lowercase_    ,				)

										logger.info("""Model exported to {}""".format(lowercase_	)	)

										lowercase_					=      remove_dup_initializers(os.path.abspath(lowercase_	)	)

										logger.info("""Deduplicated and optimized model written to {}""".format(lowercase_	)	)

										lowercase_					=      onnxruntime.InferenceSession(lowercase_	)
										lowercase_					=      ort_sess.run(
										    lowercase_    ,				{
										        """input_ids""": inputs["""input_ids"""].cpu().numpy(),
										        """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
										        """num_beams""": np.array(lowercase_	),
										        """max_length""": np.array(lowercase_	),
										        """decoder_start_token_id""": np.array(model.config.decoder_start_token_id	),
										    }    ,				)

										np.testing.assert_allclose(summary_ids.cpu().numpy()    ,				ort_out[0]    ,				rtol=1e-3    ,				atol=1e-3	)

										logger.info("""Model outputs from torch and ONNX Runtime are similar."""	)
										logger.info("""Success."""	)


def       UpperCAmelCase_					(   ):
					lowercase_					=      parse_args()
					lowercase_					=      5
					lowercase_					=      4

					# Make one log on every process with the configuration for debugging.
					logging.basicConfig(
					    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""    ,				datefmt="""%m/%d/%Y %H:%M:%S"""    ,				level=logging.INFO    ,				)

					logger.setLevel(logging.INFO	)
					transformers.utils.logging.set_verbosity_error()

					lowercase_					=      torch.device(args.device	)

					lowercase_				,       lowercase_					=      load_model_tokenizer(args.model_name_or_path    ,				lowercase_	)

					if model.config.decoder_start_token_id is None:
										raise ValueError("""Make sure that `config.decoder_start_token_id` is correctly defined"""	)

					model.to(lowercase_	)

					if args.max_length:
										lowercase_					=      args.max_length

					if args.num_beams:
										lowercase_					=      args.num_beams

					if args.output_file_path:
										lowercase_					=      args.output_file_path
					else:
										lowercase_					=      """BART.onnx"""

					logger.info("""Exporting model to ONNX"""	)
					export_and_validate_model(lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_	)


if __name__ == "__main__":
							main()










'''simple docstring'''







import math

import qiskit


def       __UpperCamelCase					(      lowercase_       :  int = 1   ,       lowercase_       :  int = 1   ,       lowercase_       :  int = 1   ):
       """simple docstring"""




       if (
           isinstance(lowercase_   ,       lowercase_   )
           or isinstance(lowercase_   ,       lowercase_   )
           or isinstance(lowercase_   ,       lowercase_   )
       ):
              raise TypeError('inputs must be integers.'   )

       if (input_a < 0) or (input_a < 0) or (carry_in < 0):
              raise ValueError('inputs must be positive.'   )

       if (
           (math.floor(lowercase_   ) != input_a)
           or (math.floor(lowercase_   ) != input_a)
           or (math.floor(lowercase_   ) != carry_in)
       ):
              raise ValueError('inputs must be exact integers.'   )

       if (input_a > 2) or (input_a > 2) or (carry_in > 2):
              raise ValueError('inputs must be less or equal to 2.'   )

       # build registers
       a_						     =			qiskit.QuantumRegister(4   ,       'qr'   )
       a_						     =			qiskit.ClassicalRegister(2   ,       'cr'   )
       # list the entries
       a_						     =			[input_a, input_a, carry_in]

       a_						     =			qiskit.QuantumCircuit(lowercase_   ,       lowercase_   )

       for i in range(0   ,       3   ):
              if entry[i] == 2:
                     quantum_circuit.h(lowercase_   )  # for hadamard entries
              elif entry[i] == 1:
                     quantum_circuit.x(lowercase_   )  # for 1 entries
              elif entry[i] == 0:
                     quantum_circuit.i(lowercase_   )  # for 0 entries

    # build the circuit
       quantum_circuit.ccx(0   ,       1   ,       3   )  # ccx = toffoli gate
       quantum_circuit.cx(0   ,       1   )
       quantum_circuit.ccx(1   ,       2   ,       3   )
       quantum_circuit.cx(1   ,       2   )
       quantum_circuit.cx(0   ,       1   )

       quantum_circuit.measure([2, 3]   ,       lowercase_   )  # measure the last two qbits

       a_						     =			qiskit.Aer.get_backend('aer_simulator'   )
       a_						     =			qiskit.execute(lowercase_   ,       lowercase_   ,       shots=1_000   )

       return job.result().get_counts(lowercase_   )


if __name__ == "__main__":
  print(f"""Total sum count for state is: {quantum_full_adder(1, 1, 1)}""")




